深度学习在头颈肿瘤多组学研究中的研究进展

Research Progress of Deep Learning in Multiomics Analyses of Head and Neck Cancers

随着检测手段不断丰富,医学上提出了多组学这一概念,用于对肿瘤发病机制和性质判断进行深入探索。然而,多组学包含的海量异构数据,仅凭人力难以全面分析。为了解决这一问题,深度学习被引入到多组学的数据分析当中。本文旨在回顾分析用于头颈肿瘤多组学分析的深度学习方法,介绍深度学习在头颈部肿瘤诊治中的新进展及其临床价值,包括早期诊断、肿瘤分期、辅助外科手术、预后分析等方面的应用与进展,总结深度学习在头颈肿瘤中面临的挑战和发展方向。

Following the development of detection technology,the conception of multiomics has been put forward,which refers to the combination of multiple sources of information.However,multiomics contains a huge amount of data,which is difficult to be analyzed by manpower in an all-round way.In order to solve this problem,deep learning has been applied into the multiomics data analysis.The purpose of this paper is to review deep learning methods commonly used in the multiomics analysis for head and neck cancers,so as to introduce the application and progress of deep learning in early diagnosis,staging,surgical assistance and prognosis,as well as summarize the challenges and future development of deep learning in head and neck cancers.